Data-driven insights for circular and sustainable food supply chains: An empirical exploration of big data and predictive analytics in enhancing social sustainability performance

Although the circular economy is commonly used among industries in developing countries to achieve carbon neutrality targets, its impact on social sustainability must be clarified. Stakeholders (for instance, community stakeholders) have been observed to be unaware of the focal firm's circular supply chain activities. Because this gap has not been generally reflected in the literature, it is critical to perform an empirical study to bridge the gap between theory and practice. The goal of this research was to determine whether new technologies such as big data and predictive analytics might influence an organization's propensity to share information related to circular economy practices with stakeholders as well as to increase connectivity with those stakeholders in the Industry 4.0 era. We also investigated whether these actions could increase stakeholder trust and engagement and social sustainability as a result. We tested our theoretical model using samples from food supply chain firms in South Africa. Confirmatory factor analysis was conducted using WarpPLS 7.0 software. The findings show that firms that deploy big data and predictive analytics are more likely to share information related to the circular economy with stakeholders and that these firms are also well-connected with those stakeholders, resulting in increased trust and engagement. This, in turn, contributes to the social sustainability of supply chains. Our research has made a significant contribution by encouraging a theoretical debate regarding the willingness to share information regarding the circular economy and the social sustainability of the supply chain.